The present embodiments relate to filtering medical diagnostic images. Filtering reduces noise in medical diagnostic images by smoothing the image.
Medical images may be subject to various types of noise. For example, ultrasound images contain speckle due to the phased array imaging technique. As another example, X-ray fluoroscopy images may show quantal noise and intrinsic electronic semiconductor noise from the X-ray detector.
Filtering in the spatial or temporal domain may reduce noise. A kernel with fixed weights is applied to each pixel value of a medical image. The relative contribution of spatially or temporally adjacent pixel values is based on the fixed weights. Various kernels have been used. For a spatial example, a Gaussian kernel more heavily weights pixel values adjacent a selected location than spaced from the location. For a temporal example, infinite impulse response filtering using a fractional weight and the inverse of the weight combines image data from different times.
Adaptive filtering smoothes a medical image as a function of the data representing the image. Edges or structures may be better maintained with adaptive filtering. The weights of the kernel adapt as a function of the image data. For spatial filtering, the kernel adapts based on the distribution of the pixel or voxel values in the proximity of the pixel or voxel being processed. Examples include bilateral filtering using geometric closeness and similarity in pixel or voxel values. Each weight is a function of how close the kernel location is to the location being filtered and how close the display value of the kernel location is to the display value being filtered. However, noise at different times is not considered.
For temporal filtering, the fractional weight and the corresponding inverse adapt as a function of difference in display values at different times. For example, sliding weighted temporal averaging (e.g., GCM) responds to movement between images. If a given pixel or voxel value is sufficiently different from a pixel or voxel value in the same location at a different time, movement has occurred. The fractional weight or the inverse applied to the older or earlier in time image data is reduced, emphasizing the current image data. Blurring due to movement may be avoided. However, a constant geometry is assumed.